A Fast Predictive Learner Model of Oxygen Content in Flue Gas for Municipal Solid Waste Incineration Process

To predict the oxygen content in flue gas quickly and accurately during the municipal solid waste incineration (MSWI), the dynamic pruning strategy based on mutual information (MI) is used to build a learner model of deep stochastic configuration network (DSCN) in this paper. This learning model consists of two parts. One is to select the characteristic variables of the oxygen content in flue gas through the improved sailfish optimizer (SFO) with t-distribution. The other is to use MI to prune hidden nodes dynamically in a layer-by-layer manner during the training of DSCN, and then obtain a prediction model of the oxygen content in flue gas. The experimental results show that the improved SFO can select the optimal characteristic variables, MI can effectively reduce the complexity of the DSCN after pruning and achieve the rapid and accurate prediction of oxygen content in flue gas, which lays a foundation for timely optimization and adjustment of incineration conditions.